Team MISTIS

Members
Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
Contracts and Grants with Industry
Other Grants and Activities
Dissemination
Bibliography

Bibliography

Major publications by the team in recent years

[1]
C. Amblard, S. Girard.
Estimation procedures for a semiparametric family of bivariate copulas, in: Journal of Computational and Graphical Statistics, 2005, vol. 14, no 2, p. 1–15.
[2]
C. Bouveyron, S. Girard, C. Schmid.
High dimensional data clustering, in: Computational Statistics and Data Analysis, 2007, vol. 52, p. 502–519.
[3]
C. Bouveyron, S. Girard, C. Schmid.
High dimensional discriminant analysis, in: Communication in Statistics - Theory and Methods, 2007, vol. 36, no 14.
[4]
G. Celeux, S. Chrétien, F. Forbes, A. Mkhadri.
A Component-wise EM Algorithm for Mixtures, in: Journal of Computational and Graphical Statistics, 2001, vol. 10, p. 699–712.
[5]
G. Celeux, F. Forbes, N. Peyrard.
EM procedures using mean field-like approximations for Markov model-based image segmentation, in: Pattern Recognition, 2003, vol. 36, no 1, p. 131-144.
[6]
B. Chalmond, S. Girard.
Nonlinear modeling of scattered multivariate data and its application to shape change, in: IEEE Trans. PAMI, 1999, vol. 21(5), p. 422–432.
[7]
F. Forbes, G. Fort.
Combining Monte Carlo and Mean field like methods for inference in hidden Markov Random Fields, in: IEEE trans. PAMI, 2007, vol. 16, no 3, p. 824-837.
[8]
F. Forbes, N. Peyrard.
Hidden Markov Random Field Model Selection Criteria based on Mean Field-like Approximations, in: in IEEE trans. PAMI, August 2003, vol. 25(9), p. 1089–1101.
[9]
G. Fort, E. Moulines.
Convergence of the Monte-Carlo EM for curved exponential families, in: Annals of Statistics, 2003, vol. 31, no 4, p. 1220-1259.
[10]
S. Girard.
A Hill type estimate of the Weibull tail-coefficient, in: Communication in Statistics - Theory and Methods, 2004, vol. 33, no 2, p. 205–234.

Publications of the year

Articles in International Peer-Reviewed Journal

[11]
C. Amblard, S. Girard.
A new bivariate extension of FGM copulas, in: Metrika, To appear, 2008.
[12]
C. Bernard-Michel, L. Gardes, S. Girard.
A Note on Sliced Inverse Regression with regularizations, in: Biometrics, 2008, vol. 64, p. 982–986.
[13]
C. Bernard-Michel, L. Gardes, S. Girard.
Gaussian regularized Sliced Inverse Regression, in: Statistics and Computing, To appear, 2008.
[14]
J. Blanchet, F. Forbes.
Triplet Markov fields for the supervised classification of complex structure data, in: IEEE trans. on Pattern Analyis and Machine Intelligence, 2008, vol. 30(6), p. 1055–1067.
[15]
G. Celeux, J. Durand.
Selecting Hidden Markov Model State Number with Cross-Validated Likelihood, in: Computational Statistics, 2008, vol. 23(4), p. 541–564.
[16]
J. Diebolt, L. Gardes, S. Girard, A. Guillou.
Bias-reduced estimators of the Weibull tail-coefficient, in: Test, 2008, vol. 17, p. 311–331.
[17]
J. Diebolt, L. Gardes, S. Girard, A. Guillou.
Bias-reduced extreme quantiles estimators of Weibull distributions, in: Journal of Statistical Planning and Inference, 2008, vol. 138, p. 1389–1401.
[18]
L. Gardes, S. Girard.
A moving window approach for nonparametric estimation of the conditional tail index, in: Journal of Multivariate Analysis, 2008, vol. 99, p. 2368–2388.
[19]
L. Gardes, S. Girard.
Estimation of the Weibull tail-coefficient with linear combination of upper order statistics, in: Journal of Statistical Planning and Inference, 2008, vol. 139, p. 1416–1427.
[20]
S. Girard, P. Jacob.
A Note on extreme values and kernel estimators of sample boundaries, in: Statistics and Probability Letters, 2008, vol. 78, p. 1634–1638.
[21]
S. Girard, P. Jacob.
Frontier estimation via kernel regression on high power-transformed data, in: Journal of Multivariate Analysis, 2008, vol. 99, p. 403–420.
[22]
S. Girard, L. Menneteau.
Smoothed extreme value estimators of non-uniform point processes boundaries with application to star-shaped supports estimation, in: Communication in Statistics - Theory and Methods, 2008, vol. 37, p. 881–897.
[23]
B. Scherrer, M. Dojat, F. Forbes, C. Garbay.
Agentification of Markov Model Based Segmentation: Application to MRI Brain Scans, in: Artificial Intelligence in Medicine (AIM), 2008.
[24]
M. Vignes, F. Forbes.
Gene clustering via integrated Markov models combining individual and pairwise features, in: IEEE trans. on Computational Biology and Bioinformatics, To appear, 2008.

International Peer-Reviewed Conference/Proceedings

[25]
S. Anquetin, B. Boudevillain, D. Ceresetti, J. Creutin, A. Godart, B. Hingray, G. Molinié, E. Leblois, C. Bernard-Michel, S. Girard, L. Gardes.
Rainfall features, forcing and estimation over the Cévennes-Vivarais region, in: 2th HyMeX workshop, Palaiseau, France, juin 2008.
[26]
E. Arnaud, H. Christensen, Y. Lu, J. Barker, V. Khalidov, M. Hansard, B. Holveck, H. Mathieu, R. Narasimha, E. Taillant, F. Forbes, R. Horaud.
The CAVA corpus : synchronised stereoscopic and binaural datasets with head movements, in: ACM/IEEE International Conference on Multimodal Interfaces (ICMI 08), 2008, p. 109-116.
[27]
J. Benediktsson, J. Chanussot, M. Fauvel.
Adaptive pixel neighborhood definition for the classification of hyperspectral images with support vector machines and composite kernel, in: 15th IEEE International Conference on Image Processing, San Diego, Etats-Unis, octobre 2008.
[28]
C. Bernard-Michel, S. Douté, L. Gardes, S. Girard.
Inverting hyperspectral images with Gaussian Regularized Sliced Inverse Regression, in: 16th European Symposium on Artificial Neural Networks, Bruges, Belgique, avril 2008, p. 463–468.
[29]
C. Bernard-Michel, L. Gardes, S. Girard.
Regularization methods for Sliced Inverse Regression, in: 8th International Conference on Operations Research, Havana, Cuba, février 2008.
[30]
C. Bouveyron, S. Girard.
Robust supervised classification with Gaussian mixtures: learning from data with uncertain labels, in: Compstat, 18th symposium of the IASC, Porto, Portugal, aout 2008.
[31]
V. Ciriza, L. Donini, J. Durand, S. Girard.
A statistical model for optimizing power consumption of printers, in: XIG R & T Conference, Xerox Corporation, Webster, USA, mai 2008.
[32]
V. Ciriza, L. Donini, J. Durand, S. Girard.
A statistical model for optimizing power consumption of printers, in: Joint Meeting of the Statiscal Society of Canada and the Société Française de Statistique, Ottawa, Canada, mai 2008.
[33]
L. Gardes, S. Girard, A. Lekina.
A moving window approach for nonparametric estimation of extreme level curves, in: 18th conference of the Intenational Federation of Operational Research Societies, Sandton, Afrique du Sud, juillet 2008.
[34]
S. Girard, P. Jacob.
Frontier estimation via regression on high power-transformed data, in: Joint Meeting of the Statiscal Society of Canada and the Société Française de Statistique, Ottawa, Canada, mai 2008.
[35]
V. Khalidov, F. Forbes, M. Hansard, E. Arnaud, R. Horaud.
Audio-Visual clustering for 3D speaker localization, in: 5th joint Workshop on Machine Learning and Multimodal Interaction MLMI 2008, Utrecht, The Netherlands, 2008, p. 86-97.
[36]
V. Khalidov, F. Forbes, M. Hansard, E. Arnaud, R. Horaud.
Detection and Localization of 3D Audio-Visual Objects Using Unsupervised Clustering, in: ACM/IEEE International Conference on Multimodal Interfaces (ICMI 08), 2008, p. 217-224.
[37]
R. Narasimha, E. Arnaud, F. Forbes, R. Horaud.
Cooperative Disparity and object boundary estimation, in: 15th IEEE Int. Conf. Imag. Proc. ICIP 08, San Diego, USA, 2008, p. 1784–1787.
[38]
B. Scherrer, F. Forbes, M. Dojat, C. Garbay.
Fully Bayesian Joint Model for MR Brain Scan Tissue and Structure Segmentation. Received the Young Investigator Award in Segmentation, in: MICCAI 2008, New-York, USA, 2008, p. 1066-74.

Other Publications

[39]
C. Bernard-Michel, S. Douté, M. Fauvel, L. Gardes, S. Girard.
Retrieval of Mars surface physical properties from OMEGA hyperspectral images using Regularized Sliced Inverse Regression, 2008
http://hal.inria.fr/inria-00276116/fr/.
[40]
L. Gardes, S. Girard, A. Guillou.
On the asymptotic normality of extreme-value estimators in the phi-tail distributions model, 2008
http://hal.archives-ouvertes.fr/hal-00340661/fr/.
[41]
L. Gardes, S. Girard, A. Lekina.
Functional nonparametric estimation of conditional extreme quantiles, 2008
http://hal.archives-ouvertes.fr/hal-00289996/fr/.

References in notes

[42]
C. Biernacki, G. Celeux, G. Govaert, F. Langrognet.
Model-Based Cluster and Discriminant Analysis with the MIXMOD Software, in: Computational Statistics and Data Analysis, 2006, vol. 51, no 2, p. 587–600.
[43]
C. Bouveyron.
Modélisation et classification des données de grande dimension. Application à l'analyse d'images, Ph. D. Thesis, Université Grenoble 1, septembre 2006
http://tel.archives-ouvertes.fr/tel-00109047.
[44]
C. Chen, F. Forbes, O. Francois.
FASTRUCT: Model-based clustering made faster, in: Molecular Ecology Notes, 2006, vol. 6, p. 980–983.
[45]
P. Embrechts, C. Klüppelberg, T. Mikosh.
Modelling Extremal Events, Applications of Mathematics, Springer-Verlag, 1997, vol. 33.
[46]
F. Ferraty, P. Vieu.
Nonparametric Functional Data Analysis: Theory and Practice, Springer Series in Statistics, Springer, 2006.
[47]
O. Francois, S. Ancelet, G. Guillot.
Bayesian clustering using Hidden Markov Random Fields in spatial genetics, in: Genetics, 2006, p. 805–816.
[48]
L. Gardes.
Estimation d'une fonction quantile extrême, Ph. D. Thesis, Université Montpellier 2, october 2003.
[49]
M. Garrido.
Modélisation des événements rares et estimation des quantiles extrêmes, méthodes de sélection de modèles pour les queues de distribution, Ph. D. Thesis, Université Grenoble 1, juin 2002
http://mistis.inrialpes.fr/people/girard/Fichiers/theseGarrido.pdf.
[50]
S. Girard.
Construction et apprentissage statistique de modèles auto-associatifs non-linéaires. Application à l'identification d'objets déformables en radiographie. Modélisation et classification, Ph. D. Thesis, Université de Cery-Pontoise, octobre 1996.
[51]
K. Li.
Sliced inverse regression for dimension reduction, in: Journal of the American Statistical Association, 1991, vol. 86, p. 316–327.
[52]
R. Nelsen.
An introduction to copulas, Lecture Notes in Statistics, Springer-Verlag , New-York, 1999, vol. 139.
[53]
J. Pritchard, M. Stephens, P. Donnelly.
Inference of Population Structure Using Multilocus Genotype Data, in: Genetics, 2000, vol. 155, p. 945–959.
[54]
B. Scherrer, M. Dojat, F. Forbes, C. Garbay.
LOCUS: LOcal Cooperative Unified Segmentation of MRI brain scans, in: MICCAI 2007, Brisbane, Australia, 2007, p. 219-227.

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